# OpenFinOps Observability Platform Implementation Guide

## 🚀 Quick Start

This guide provides step-by-step instructions for implementing the OpenFinOps Observability Platform in your enterprise environment.

## 📋 Prerequisites

### System Requirements
- **Python**: 3.8+ with pip
- **Node.js**: 16+ (for web frontend)
- **PHP**: 7.4+ (for web server)
- **Database**: PostgreSQL 12+ or MongoDB 4.4+
- **Memory**: Minimum 8GB RAM (16GB+ recommended)
- **Storage**: 100GB+ for metrics storage
- **Network**: Stable internet connection for cloud integrations

### Required Permissions
- **Administrative access** to target systems
- **API credentials** for cloud providers (AWS, Azure, GCP)
- **Network access** to monitoring targets
- **Database creation permissions**

## 🛠️ Installation Steps

### Step 1: Environment Setup

#### 1.1 Clone the Repository
```bash
git clone https://github.com/rdmurugan/openfinops.git
cd openfinops
```

#### 1.2 Create Virtual Environment
```bash
# Create virtual environment
python -m venv openfinops-env

# Activate virtual environment
# On Linux/Mac:
source openfinops-env/bin/activate
# On Windows:
openfinops-env\Scripts\activate
```

#### 1.3 Install Dependencies
```bash
# Install core dependencies
pip install -r requirements.txt

# Install observability-specific dependencies
pip install -r src/observability/requirements.txt

# Verify installation
python -c "from src.observability import ObservabilityHub; print('Installation successful!')"
```

### Step 2: Configuration

#### 2.1 Create Configuration File
```bash
# Copy example configuration
cp config/observability.example.yaml config/observability.yaml

# Edit configuration
nano config/observability.yaml
```

#### 2.2 Basic Configuration (`config/observability.yaml`)
```yaml
# OpenFinOps Observability Configuration

# Core Settings
observability:
  name: "Production Observability"
  environment: "production"  # development, staging, production
  log_level: "INFO"
  data_retention_days: 30

# Database Configuration
database:
  type: "postgresql"  # postgresql, mongodb
  host: "localhost"
  port: 5432
  database: "openfinops_observability"
  username: "openfinops_user"
  password: "your_secure_password"

# Monitoring Targets
monitoring:
  # LLM Training Clusters
  training_clusters:
    - name: "gpu-cluster-1"
      endpoint: "https://cluster1.your-domain.com"
      nodes:
        - "node-1.cluster1.your-domain.com"
        - "node-2.cluster1.your-domain.com"
      monitoring_interval: 30  # seconds

  # RAG Systems
  rag_systems:
    - name: "production-rag"
      vector_db_url: "https://pinecone.your-domain.com"
      api_endpoint: "https://rag-api.your-domain.com"
      monitoring_interval: 60

# FinOps Configuration
finops:
  # Cloud Provider APIs
  aws:
    access_key_id: "your_aws_access_key"
    secret_access_key: "your_aws_secret_key"
    region: "us-west-2"

  azure:
    subscription_id: "your_azure_subscription"
    client_id: "your_azure_client_id"
    client_secret: "your_azure_secret"

  gcp:
    project_id: "your_gcp_project"
    credentials_file: "path/to/gcp-credentials.json"

  # AI Platform APIs
  openai:
    api_key: "your_openai_api_key"
    organization: "your_org_id"

  anthropic:
    api_key: "your_anthropic_api_key"

# Alerting Configuration
alerting:
  channels:
    email:
      smtp_server: "smtp.your-domain.com"
      smtp_port: 587
      username: "alerts@your-domain.com"
      password: "your_email_password"
      recipients:
        - "ops-team@your-domain.com"
        - "cto@your-domain.com"

    slack:
      webhook_url: "https://hooks.slack.com/your-webhook"
      channel: "#observability-alerts"

    pagerduty:
      integration_key: "your_pagerduty_key"

# Security Settings
security:
  api_key: "your_api_key_here"
  encryption_key: "your_32_char_encryption_key_here"
  ssl_verification: true
  audit_logging: true

# Web Interface
web:
  host: "0.0.0.0"
  port: 8888
  debug: false
  cors_origins:
    - "https://your-domain.com"
    - "https://observability.your-domain.com"
```

### Step 3: Database Setup

#### 3.1 PostgreSQL Setup
```bash
# Install PostgreSQL (Ubuntu/Debian)
sudo apt update
sudo apt install postgresql postgresql-contrib

# Create database and user
sudo -u postgres psql << EOF
CREATE DATABASE openfinops_observability;
CREATE USER openfinops_user WITH PASSWORD 'your_secure_password';
GRANT ALL PRIVILEGES ON DATABASE openfinops_observability TO openfinops_user;
\q
EOF
```

#### 3.2 Initialize Database Schema
```bash
# Run database migrations
python src/observability/setup_database.py

# Verify tables created
python -c "
from src.observability.observability_hub import ObservabilityHub
hub = ObservabilityHub()
print('Database setup complete!')
"
```

### Step 4: Core System Deployment

#### 4.1 Start Observability Hub
```bash
# Start the main observability service
python -m src.observability.observability_hub --config config/observability.yaml

# Verify service is running
curl http://localhost:8888/health
```

#### 4.2 Deploy LLM Monitoring
```python
# Deploy LLM observability components
from src.observability.llm_observability import LLMObservabilityHub

# Initialize LLM monitoring
llm_hub = LLMObservabilityHub(config_path="config/observability.yaml")

# Register training cluster
llm_hub.register_training_cluster(
    cluster_name="gpu-cluster-1",
    nodes=["node-1.example.com", "node-2.example.com"]
)

# Start monitoring
llm_hub.start_monitoring()
print("LLM monitoring deployed successfully!")
```

#### 4.3 Deploy FinOps Monitoring
```python
# Deploy financial operations monitoring
from src.observability.finops_dashboards import LLMFinOpsDashboardCreator

# Initialize FinOps
finops = LLMFinOpsDashboardCreator()

# Configure cost tracking
finops.setup_cost_tracking(
    cloud_providers=["aws", "azure", "gcp"],
    ai_platforms=["openai", "anthropic"]
)

# Start cost monitoring
finops.start_cost_monitoring()
print("FinOps monitoring deployed successfully!")
```

## 🔧 Advanced Configuration

### Distributed Deployment

#### 4.4 Multi-Node Setup
```yaml
# config/distributed.yaml
distributed:
  mode: "cluster"
  nodes:
    - role: "coordinator"
      host: "obs-coordinator.your-domain.com"
      port: 8888
    - role: "worker"
      host: "obs-worker-1.your-domain.com"
      port: 8889
    - role: "worker"
      host: "obs-worker-2.your-domain.com"
      port: 8890

  load_balancer:
    enabled: true
    algorithm: "round_robin"
    health_check_interval: 30
```

#### 4.5 Kubernetes Deployment
```yaml
# k8s/observability-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: openfinops-observability
  namespace: observability
spec:
  replicas: 3
  selector:
    matchLabels:
      app: openfinops-observability
  template:
    metadata:
      labels:
        app: openfinops-observability
    spec:
      containers:
      - name: observability-hub
        image: openfinops/observability:latest
        ports:
        - containerPort: 8888
        env:
        - name: CONFIG_PATH
          value: "/config/observability.yaml"
        volumeMounts:
        - name: config-volume
          mountPath: /config
        resources:
          requests:
            memory: "2Gi"
            cpu: "1"
          limits:
            memory: "4Gi"
            cpu: "2"
      volumes:
      - name: config-volume
        configMap:
          name: observability-config

---
apiVersion: v1
kind: Service
metadata:
  name: observability-service
  namespace: observability
spec:
  selector:
    app: openfinops-observability
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8888
  type: LoadBalancer
```

## 📊 Monitoring Implementation

### Step 5: LLM Training Monitoring

#### 5.1 Integrate Training Pipeline
```python
# training_integration.py
from src.observability.llm_observability import LLMObservabilityHub

class TrainingObservabilityIntegration:
    def __init__(self):
        self.obs_hub = LLMObservabilityHub()

    def track_training_step(self, step_data):
        """Track individual training steps"""
        self.obs_hub.track_training_metrics(
            model_id="gpt-custom-v1",
            epoch=step_data["epoch"],
            step=step_data["step"],
            loss=step_data["loss"],
            learning_rate=step_data["lr"],
            gpu_memory_usage=step_data["gpu_memory"],
            batch_size=step_data["batch_size"]
        )

    def track_checkpoint(self, checkpoint_data):
        """Track model checkpoints"""
        self.obs_hub.save_checkpoint_metrics(
            model_id="gpt-custom-v1",
            checkpoint_path=checkpoint_data["path"],
            validation_loss=checkpoint_data["val_loss"],
            model_size=checkpoint_data["size_mb"]
        )

# Usage in training loop
obs_integration = TrainingObservabilityIntegration()

for epoch in range(num_epochs):
    for step, batch in enumerate(dataloader):
        # Training step
        loss = train_step(batch)

        # Track metrics
        obs_integration.track_training_step({
            "epoch": epoch,
            "step": step,
            "loss": loss.item(),
            "lr": optimizer.param_groups[0]['lr'],
            "gpu_memory": torch.cuda.memory_allocated(),
            "batch_size": batch.size(0)
        })
```

#### 5.2 RAG System Monitoring
```python
# rag_monitoring.py
from src.observability.llm_observability import LLMObservabilityHub

class RAGObservabilityIntegration:
    def __init__(self):
        self.obs_hub = LLMObservabilityHub()

    def track_rag_query(self, query_data):
        """Track RAG system queries"""
        self.obs_hub.track_rag_metrics(
            system_id="production-rag",
            query=query_data["query"],
            retrieval_time=query_data["retrieval_ms"],
            generation_time=query_data["generation_ms"],
            relevance_score=query_data["relevance"],
            num_retrieved_docs=query_data["doc_count"],
            total_tokens=query_data["tokens"]
        )

    def track_vector_db_performance(self, vector_data):
        """Track vector database performance"""
        self.obs_hub.track_vector_db_metrics(
            db_id="pinecone-prod",
            query_latency=vector_data["latency_ms"],
            index_size=vector_data["index_size"],
            memory_usage=vector_data["memory_mb"],
            throughput=vector_data["queries_per_second"]
        )

# Usage in RAG system
rag_obs = RAGObservabilityIntegration()

def rag_query(user_query):
    start_time = time.time()

    # Retrieve relevant documents
    retrieval_start = time.time()
    documents = vector_db.query(user_query, top_k=5)
    retrieval_time = (time.time() - retrieval_start) * 1000

    # Generate response
    generation_start = time.time()
    response = llm.generate(user_query, documents)
    generation_time = (time.time() - generation_start) * 1000

    # Track metrics
    rag_obs.track_rag_query({
        "query": user_query,
        "retrieval_ms": retrieval_time,
        "generation_ms": generation_time,
        "relevance": calculate_relevance_score(documents),
        "doc_count": len(documents),
        "tokens": count_tokens(response)
    })

    return response
```

### Step 6: FinOps Implementation

#### 6.1 Cost Tracking Setup
```python
# finops_integration.py
from src.observability.finops_dashboards import LLMFinOpsDashboardCreator

class FinOpsIntegration:
    def __init__(self):
        self.finops = LLMFinOpsDashboardCreator()

    def track_cloud_costs(self):
        """Track cloud infrastructure costs"""
        # AWS costs
        aws_costs = self.get_aws_costs()
        self.finops.update_cloud_costs("aws", aws_costs)

        # Azure costs
        azure_costs = self.get_azure_costs()
        self.finops.update_cloud_costs("azure", azure_costs)

        # GCP costs
        gcp_costs = self.get_gcp_costs()
        self.finops.update_cloud_costs("gcp", gcp_costs)

    def track_api_costs(self, api_call_data):
        """Track AI platform API costs"""
        self.finops.track_api_usage(
            platform=api_call_data["platform"],  # "openai", "anthropic"
            model=api_call_data["model"],
            tokens_input=api_call_data["input_tokens"],
            tokens_output=api_call_data["output_tokens"],
            cost=api_call_data["cost_usd"]
        )

    def generate_cost_optimization_report(self):
        """Generate cost optimization recommendations"""
        return self.finops.generate_optimization_report()

# Usage
finops = FinOpsIntegration()

# Track API usage
def track_openai_call(prompt, response):
    finops.track_api_costs({
        "platform": "openai",
        "model": "gpt-4",
        "input_tokens": count_tokens(prompt),
        "output_tokens": count_tokens(response),
        "cost_usd": calculate_openai_cost(prompt, response)
    })

# Generate daily cost reports
def daily_cost_report():
    finops.track_cloud_costs()
    report = finops.generate_cost_optimization_report()
    send_report_to_stakeholders(report)
```

### Step 7: Alerting Configuration

#### 7.1 Custom Alert Rules
```python
# alert_configuration.py
from src.observability.alerting_engine import AlertingEngine

class CustomAlertRules:
    def __init__(self):
        self.alerting = AlertingEngine()

    def setup_training_alerts(self):
        """Setup alerts for training issues"""

        # Training loss divergence alert
        self.alerting.add_rule(
            name="training_loss_divergence",
            condition="training_loss > baseline_loss * 1.5",
            severity="critical",
            description="Training loss is diverging",
            channels=["email", "slack"]
        )

        # GPU utilization alert
        self.alerting.add_rule(
            name="low_gpu_utilization",
            condition="gpu_utilization < 50",
            severity="warning",
            description="GPU utilization is below optimal threshold",
            channels=["slack"]
        )

        # Memory exhaustion alert
        self.alerting.add_rule(
            name="gpu_memory_high",
            condition="gpu_memory_usage > 90",
            severity="critical",
            description="GPU memory usage is critically high",
            channels=["email", "pagerduty"]
        )

    def setup_cost_alerts(self):
        """Setup cost-related alerts"""

        # Monthly budget alert
        self.alerting.add_rule(
            name="monthly_budget_exceeded",
            condition="monthly_cost > monthly_budget",
            severity="critical",
            description="Monthly AI operations budget exceeded",
            channels=["email", "slack"]
        )

        # Unusual spending pattern
        self.alerting.add_rule(
            name="cost_anomaly",
            condition="daily_cost > avg_daily_cost * 2",
            severity="warning",
            description="Unusual spending pattern detected",
            channels=["email"]
        )

# Setup alerts
alert_rules = CustomAlertRules()
alert_rules.setup_training_alerts()
alert_rules.setup_cost_alerts()
```

## 🌐 Web Interface Deployment

### Step 8: Frontend Setup

#### 8.1 PHP Web Server Configuration
```bash
# Install PHP and dependencies
sudo apt install php php-fpm nginx

# Configure Nginx
sudo nano /etc/nginx/sites-available/openfinops-observability
```

```nginx
# /etc/nginx/sites-available/openfinops-observability
server {
    listen 80;
    server_name observability.your-domain.com;
    root /var/www/openfinops/website;
    index index.php index.html;

    location / {
        try_files $uri $uri/ /index.php?$query_string;
    }

    location ~ \.php$ {
        fastcgi_pass unix:/var/run/php/php7.4-fpm.sock;
        fastcgi_index index.php;
        fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name;
        include fastcgi_params;
    }

    location /api/ {
        proxy_pass http://localhost:8888/;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }
}
```

#### 8.2 Enable Site and Restart Services
```bash
# Enable site
sudo ln -s /etc/nginx/sites-available/openfinops-observability /etc/nginx/sites-enabled/
sudo nginx -t
sudo systemctl restart nginx
sudo systemctl restart php7.4-fpm

# Copy web files
sudo cp -r website/* /var/www/openfinops/website/
sudo chown -R www-data:www-data /var/www/openfinops/
```

## 🔐 Security Implementation

### Step 9: Security Hardening

#### 9.1 API Authentication
```python
# security_config.py
from src.observability.security_monitor import SecurityMonitor

class SecurityImplementation:
    def __init__(self):
        self.security = SecurityMonitor()

    def setup_authentication(self):
        """Setup API authentication"""
        self.security.configure_auth({
            "method": "jwt",
            "secret_key": "your_jwt_secret_key",
            "expiration": 3600,  # 1 hour
            "refresh_enabled": True
        })

    def setup_rbac(self):
        """Setup role-based access control"""
        self.security.define_roles({
            "admin": ["read", "write", "delete", "configure"],
            "operator": ["read", "write"],
            "viewer": ["read"]
        })

    def setup_audit_logging(self):
        """Setup comprehensive audit logging"""
        self.security.configure_audit({
            "log_all_api_calls": True,
            "log_data_access": True,
            "log_configuration_changes": True,
            "retention_days": 90
        })

# Implement security
security_impl = SecurityImplementation()
security_impl.setup_authentication()
security_impl.setup_rbac()
security_impl.setup_audit_logging()
```

#### 9.2 SSL/TLS Configuration
```bash
# Install Certbot for Let's Encrypt
sudo apt install certbot python3-certbot-nginx

# Get SSL certificate
sudo certbot --nginx -d observability.your-domain.com

# Auto-renewal setup
sudo crontab -e
# Add: 0 12 * * * /usr/bin/certbot renew --quiet
```

## 📊 Dashboard Configuration

### Step 10: Custom Dashboards

#### 10.1 Executive Dashboard
```python
# dashboard_config.py
from src.observability.llm_dashboards import LLMDashboardCreator

class CustomDashboards:
    def __init__(self):
        self.dashboard_creator = LLMDashboardCreator()

    def create_executive_dashboard(self):
        """Create high-level executive dashboard"""
        dashboard = self.dashboard_creator.create_dashboard("Executive Overview")

        # Key metrics
        dashboard.add_metric_card("Monthly AI Spend", "$247.8K", trend="+12%")
        dashboard.add_metric_card("Active Models", "23", trend="+3")
        dashboard.add_metric_card("Cost per Query", "$0.043", trend="-8%")
        dashboard.add_metric_card("Uptime", "99.97%", trend="stable")

        # Charts
        dashboard.add_chart(
            title="Monthly Cost Trend",
            chart_type="line",
            data_source="monthly_costs"
        )

        dashboard.add_chart(
            title="Model Performance",
            chart_type="bar",
            data_source="model_metrics"
        )

        return dashboard

    def create_technical_dashboard(self):
        """Create detailed technical dashboard"""
        dashboard = self.dashboard_creator.create_dashboard("Technical Metrics")

        # Performance metrics
        dashboard.add_real_time_chart(
            title="GPU Utilization",
            metric="gpu_usage",
            refresh_interval=5
        )

        dashboard.add_real_time_chart(
            title="Training Loss",
            metric="training_loss",
            refresh_interval=10
        )

        dashboard.add_table(
            title="Active Training Jobs",
            data_source="training_jobs",
            columns=["Job ID", "Model", "Progress", "ETA", "Status"]
        )

        return dashboard

# Create dashboards
dashboard_manager = CustomDashboards()
exec_dashboard = dashboard_manager.create_executive_dashboard()
tech_dashboard = dashboard_manager.create_technical_dashboard()
```

## 🔄 Integration Examples

### Step 11: Platform Integrations

#### 11.1 OpenAI Integration
```python
# openai_integration.py
import openai
from src.observability.llm_observability import LLMObservabilityHub

class OpenAIObservabilityWrapper:
    def __init__(self, api_key, obs_hub):
        self.client = openai.OpenAI(api_key=api_key)
        self.obs_hub = obs_hub

    def chat_completion_with_monitoring(self, **kwargs):
        """OpenAI chat completion with monitoring"""
        start_time = time.time()

        try:
            response = self.client.chat.completions.create(**kwargs)

            # Track successful call
            self.obs_hub.track_api_call(
                platform="openai",
                model=kwargs.get("model", "gpt-3.5-turbo"),
                input_tokens=response.usage.prompt_tokens,
                output_tokens=response.usage.completion_tokens,
                latency_ms=(time.time() - start_time) * 1000,
                cost=self._calculate_cost(response.usage, kwargs.get("model")),
                status="success"
            )

            return response

        except Exception as e:
            # Track failed call
            self.obs_hub.track_api_call(
                platform="openai",
                model=kwargs.get("model", "gpt-3.5-turbo"),
                latency_ms=(time.time() - start_time) * 1000,
                status="error",
                error_message=str(e)
            )
            raise

    def _calculate_cost(self, usage, model):
        """Calculate API call cost"""
        pricing = {
            "gpt-3.5-turbo": {"input": 0.0015, "output": 0.002},
            "gpt-4": {"input": 0.03, "output": 0.06}
        }

        model_pricing = pricing.get(model, pricing["gpt-3.5-turbo"])
        input_cost = (usage.prompt_tokens / 1000) * model_pricing["input"]
        output_cost = (usage.completion_tokens / 1000) * model_pricing["output"]

        return input_cost + output_cost

# Usage
obs_hub = LLMObservabilityHub()
openai_client = OpenAIObservabilityWrapper("your-api-key", obs_hub)

response = openai_client.chat_completion_with_monitoring(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello, world!"}]
)
```

#### 11.2 Kubernetes Integration
```yaml
# k8s-monitoring-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: observability-k8s-config
data:
  config.yaml: |
    kubernetes:
      cluster_name: "production-k8s"
      namespace_monitoring:
        - "ai-training"
        - "ml-inference"
        - "data-processing"

      pod_metrics:
        - cpu_usage
        - memory_usage
        - gpu_usage
        - network_io
        - disk_io

      service_monitoring:
        - service_latency
        - request_rate
        - error_rate
        - availability

      alerts:
        - name: "pod_memory_high"
          condition: "memory_usage > 80"
          severity: "warning"
        - name: "pod_cpu_high"
          condition: "cpu_usage > 90"
          severity: "critical"

---
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: observability-agent
spec:
  selector:
    matchLabels:
      app: observability-agent
  template:
    metadata:
      labels:
        app: observability-agent
    spec:
      containers:
      - name: agent
        image: openfinops/k8s-agent:latest
        env:
        - name: CLUSTER_NAME
          value: "production-k8s"
        - name: OBSERVABILITY_ENDPOINT
          value: "http://observability-service:8888"
        volumeMounts:
        - name: proc
          mountPath: /host/proc
          readOnly: true
        - name: sys
          mountPath: /host/sys
          readOnly: true
      volumes:
      - name: proc
        hostPath:
          path: /proc
      - name: sys
        hostPath:
          path: /sys
```

## 🧪 Testing and Validation

### Step 12: System Testing

#### 12.1 Unit Tests
```bash
# Run unit tests
python -m pytest src/observability/tests/ -v

# Run specific component tests
python -m pytest src/observability/tests/test_llm_observability.py -v
python -m pytest src/observability/tests/test_finops_dashboards.py -v
python -m pytest src/observability/tests/test_alerting_engine.py -v
```

#### 12.2 Integration Tests
```python
# integration_tests.py
import time
import unittest
from src.observability.observability_hub import ObservabilityHub
from src.observability.llm_observability import LLMObservabilityHub

class IntegrationTests(unittest.TestCase):
    def setUp(self):
        self.obs_hub = ObservabilityHub(config_path="config/test.yaml")
        self.llm_hub = LLMObservabilityHub(config_path="config/test.yaml")

    def test_end_to_end_monitoring(self):
        """Test complete monitoring pipeline"""
        # Register training cluster
        self.llm_hub.register_training_cluster(
            cluster_name="test-cluster",
            nodes=["test-node-1", "test-node-2"]
        )

        # Track training metrics
        self.llm_hub.track_training_metrics(
            model_id="test-model",
            epoch=1,
            step=100,
            loss=0.5,
            learning_rate=0.001,
            gpu_memory_usage=8000,
            batch_size=32
        )

        # Verify metrics were recorded
        metrics = self.obs_hub.get_cluster_health_summary()
        self.assertIsNotNone(metrics)
        self.assertIn("test-cluster", metrics)

    def test_alert_generation(self):
        """Test alert generation and routing"""
        # Trigger a high GPU usage alert
        self.obs_hub.update_node_metrics(
            node_id="test-node-1",
            metrics={
                "gpu_usage": 95.0,  # Above threshold
                "memory_usage": 70.0,
                "cpu_usage": 60.0
            }
        )

        # Wait for alert processing
        time.sleep(2)

        # Check if alert was generated
        alerts = self.obs_hub.get_active_alerts()
        gpu_alerts = [a for a in alerts if "gpu" in a.message.lower()]
        self.assertGreater(len(gpu_alerts), 0)

if __name__ == "__main__":
    unittest.main()
```

#### 12.3 Performance Tests
```python
# performance_tests.py
import time
import concurrent.futures
from src.observability.llm_observability import LLMObservabilityHub

def performance_test_high_volume_metrics():
    """Test system performance under high metric volume"""
    llm_hub = LLMObservabilityHub()

    def send_metrics_batch():
        for i in range(1000):
            llm_hub.track_training_metrics(
                model_id=f"model-{i % 10}",
                epoch=i % 100,
                step=i,
                loss=0.5 + (i % 50) * 0.01,
                learning_rate=0.001,
                gpu_memory_usage=8000 + (i % 1000),
                batch_size=32
            )

    # Send metrics from multiple threads
    start_time = time.time()
    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
        futures = [executor.submit(send_metrics_batch) for _ in range(10)]
        concurrent.futures.wait(futures)

    duration = time.time() - start_time
    metrics_per_second = 10000 / duration

    print(f"Performance Test Results:")
    print(f"Total metrics sent: 10,000")
    print(f"Duration: {duration:.2f} seconds")
    print(f"Metrics per second: {metrics_per_second:.2f}")

    # Assert minimum performance requirement
    assert metrics_per_second > 1000, f"Performance below threshold: {metrics_per_second}"

if __name__ == "__main__":
    performance_test_high_volume_metrics()
```

## 🚀 Production Deployment

### Step 13: Production Considerations

#### 13.1 High Availability Setup
```yaml
# ha-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: observability-ha
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 1
  selector:
    matchLabels:
      app: observability
  template:
    spec:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
          - labelSelector:
              matchExpressions:
              - key: app
                operator: In
                values:
                - observability
            topologyKey: kubernetes.io/hostname
      containers:
      - name: observability
        image: openfinops/observability:latest
        readinessProbe:
          httpGet:
            path: /health
            port: 8888
          initialDelaySeconds: 30
          periodSeconds: 10
        livenessProbe:
          httpGet:
            path: /health
            port: 8888
          initialDelaySeconds: 60
          periodSeconds: 30
```

#### 13.2 Backup and Recovery
```bash
#!/bin/bash
# backup_script.sh

# Database backup
pg_dump openfinops_observability > "backup_$(date +%Y%m%d_%H%M%S).sql"

# Configuration backup
tar -czf "config_backup_$(date +%Y%m%d_%H%M%S).tar.gz" config/

# Upload to cloud storage
aws s3 cp backup_*.sql s3://your-backup-bucket/database/
aws s3 cp config_backup_*.tar.gz s3://your-backup-bucket/config/

# Cleanup old backups (keep last 30 days)
find . -name "backup_*.sql" -mtime +30 -delete
find . -name "config_backup_*.tar.gz" -mtime +30 -delete
```

#### 13.3 Monitoring the Monitor
```python
# self_monitoring.py
from src.observability.observability_hub import ObservabilityHub

class SelfMonitoring:
    def __init__(self):
        self.obs_hub = ObservabilityHub()

    def monitor_system_health(self):
        """Monitor the observability system itself"""

        # Check database connectivity
        db_status = self.obs_hub.check_database_health()

        # Check API endpoints
        api_status = self.obs_hub.check_api_health()

        # Check disk space
        disk_usage = self.obs_hub.check_disk_usage()

        # Check memory usage
        memory_usage = self.obs_hub.check_memory_usage()

        # Generate health report
        health_report = {
            "timestamp": time.time(),
            "database": db_status,
            "api": api_status,
            "disk_usage": disk_usage,
            "memory_usage": memory_usage,
            "overall_status": "healthy" if all([
                db_status["status"] == "healthy",
                api_status["status"] == "healthy",
                disk_usage < 80,
                memory_usage < 80
            ]) else "warning"
        }

        return health_report

# Setup self-monitoring
self_monitor = SelfMonitoring()

# Run health check every 5 minutes
def periodic_health_check():
    while True:
        health = self_monitor.monitor_system_health()
        if health["overall_status"] != "healthy":
            send_alert("Observability system health issue", health)
        time.sleep(300)  # 5 minutes
```

## 📚 Troubleshooting Guide

### Common Issues and Solutions

#### Issue 1: Database Connection Errors
```bash
# Check database status
sudo systemctl status postgresql

# Check logs
sudo tail -f /var/log/postgresql/postgresql-12-main.log

# Verify connection
psql -h localhost -U openfinops_user -d openfinops_observability -c "SELECT 1;"
```

#### Issue 2: High Memory Usage
```python
# Memory optimization settings
memory_config = {
    "max_metrics_buffer": 10000,
    "metric_retention_hours": 24,
    "batch_processing_size": 100,
    "gc_collection_interval": 60
}
```

#### Issue 3: Slow Dashboard Loading
```bash
# Enable caching
redis-cli config set maxmemory 2gb
redis-cli config set maxmemory-policy allkeys-lru

# Optimize database
psql -U openfinops_user -d openfinops_observability -c "
CREATE INDEX CONCURRENTLY idx_metrics_timestamp ON metrics(timestamp);
CREATE INDEX CONCURRENTLY idx_alerts_severity ON alerts(severity);
VACUUM ANALYZE;
"
```

## ✅ Validation Checklist

Before going to production, ensure:

- [ ] All components are properly configured
- [ ] Database schema is initialized
- [ ] Security settings are hardened
- [ ] SSL/TLS certificates are installed
- [ ] Backup procedures are tested
- [ ] Monitoring alerts are configured
- [ ] Performance tests pass
- [ ] Integration tests pass
- [ ] Documentation is complete
- [ ] Team training is conducted

## 🔗 Additional Resources

- [Architecture Documentation](OBSERVABILITY_ARCHITECTURE.md)
- [API Reference](API_REFERENCE.md)
- [Configuration Guide](CONFIGURATION_GUIDE.md)
- [Best Practices](BEST_PRACTICES.md)
- [Troubleshooting Guide](TROUBLESHOOTING_GUIDE.md)

---

## 📞 Support

For implementation support:
- **Email**: support@openfinops.com
- **Documentation**: https://docs.openfinops.com
- **GitHub Issues**: https://github.com/rdmurugan/openfinops/issues

**Enterprise Support**: Contact durai@infinidatum.net for dedicated implementation assistance.